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11723
Modelling Indoor Air Carbon Dioxide (CO2)Concentration using Neural Network
Abstract:
The use of neural networks is popular in various building applications such as prediction of heating load, ventilation rate and indoor temperature. Significant is, that only few papers deal with indoor carbon dioxide (CO2) prediction which is a very good indicator of indoor air quality (IAQ). In this study, a data-driven modelling method based on multilayer perceptron network for indoor air carbon dioxide in an apartment building is developed. Temperature and humidity measurements are used as input variables to the network. Motivation for this study derives from the following issues. First, measuring carbon dioxide is expensive and sensors power consumptions is high and secondly, this leads to short operating times of battery-powered sensors. The results show that predicting CO2 concentration based on relative humidity and temperature measurements, is difficult. Therefore, more additional information is needed.
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References:

[1] K. Arnold, "Sick building syndrome solutions," Professional Safety, vol. 46, pp. 43-44, 2001.
[2] O. A. Seppänen, W. J. Fisk and M. J. Mendell, "Association of ventilation rates and CO2 concentrations with health and other responses in commercial and institutional buildings," Indoor Air, vol. 9, pp. 226- 252, 1999.
[3] Asumisterveysohje, Sosiaali- ja terveysministeriön oppaita, Sosiaali- ja terveysministeriö, Oy Edita Ab, Helsinki, 2003 (in Finnish).
[4] D. Butler, "Architects of a Low-energy Future," Nature, 452, pp. 520- 523, Apr. 2008.
[5] R. Armstrong and N. Spiller, "Synthetic biology: Living quarters," Nature, 467, pp. 916-918, Oct. 2010.
[6] N. Gershenfeld, S. Samouhos, and B. Nordman: "Intelligent Infrastructure for energy efficiency," Science, vol. 372, pp.1086-1088, Feb. 2010.
[7] R. J. Jackson, "Environment Meets Health, Again," Science, 315(5817), pp.1337, Mar. 2007.
[8] J. P. Holdren, "Energy and Sustainability," Science, 315(5813), pp. 737, Feb. 2007.
[9] S. C. Sofuoglu, "Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings," Building and Environment, vol. 43, pp. 1121-1126, 2007.
[10] H. Xie, F. Ma and Q. G. Bai, "Prediction of indoor air quality using artificial neural networks," Fifth International Conference on Natural Computation (ICNC '09), vol. 2, pp. 414-418, 2009.
[11] M. H. Kim, Y. S. Kim, J. J. Lim, J. T. Kim, S. W. Sung and C. K. Yoo, "Data-driven prediction model of indoor air quality in an underground space," Korean Journal of Chemical Engineering, vol. 27, pp. 1675- 1680, 2010.
[12] T. E. Alhanafy, F. Zaghlool and A. S. El Din Moustafa, "Neuro fuzzy modeling scheme for the prediction of air pollution," Journal of American Science, vol. 6, pp. 605-616, 2010.
[13] T. Lu and M. Viljanen, "Prediction of indoor temperature and relative humidity using neural network models: model comparison," Neural Computing & Applications, vol.18, pp. 345-357, 2009
[14] M. Kolehmainen, H. Martikainen, T. Hiltunen, and J. Ruuskanen, "Forecasting air quality parameters using hybrid neural network modelling," Environmental Monitoring and Assessment, vol. 65, pp. 277-286, 2000.
[15] M. Kolehmainen, H. Martikainen and J. Ruuskanen, "Neural networks and periodic components used in air quality forecasting," Atmospheric Environment, vol. 35, pp. 815-825, 2001.
[16] H. Niska, T. Hiltunen, M. Kolehmainen and J. Ruuskanen, "Hybrid models for forecasting air pollution episodes," International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA'03), University Technical Institute of Roanne, France April 23-25, 2003.
[17] J-P. Skön, O. Kauhanen and M. Kolehmainen, "Energy Consumption and Air Quality Monitoring System," Proceedings of the 7th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 163-167, Adelaide, Australia Dec. 6-9, 2011.
[18] S. Haykin, "Neural Networks-A Comprehensive Foundation," 2nd ed., New Jersey: Prentice-Hall Inc., 1999.
[19] R. Kohavi and F. Provost, "Glossary of terms," Machine Learning, vol. 30, pp. 271-274, 1998.
[20] C. J. Willmott, "Some Comments on the Evaluation of Model Performance," Bulletin American Meteorological Society, vol. 63, pp.1309-1313, 1982.

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